Abstract: The REALDISP dataset is devised to evaluate techniques dealing with the effects of sensor displacement in wearable activity recognition as well as to benchmark general activity recognition algorithms

The REALDISP (REAListic sensor DISPlacement) dataset has been originally collected to investigate the effects of sensor displacement in the activity recognition process in real-world settings. It builds on the concept of ideal-placement, self-placement and induced-displacement. The ideal and mutual-displacement conditions represent extreme displacement variants and thus could represent boundary conditions for recognition algorithms. In contrast, self-placement reflects a users perception of how sensors could be attached, e.g., in a sports or lifestyle application. The dataset includes a wide range of physical activities (warm up, cool down and fitness exercises), sensor modalities (acceleration, rate of turn, magnetic field and quaternions) and participants (17 subjects). Apart from investigating sensor displacement, the dataset lend itself for benchmarking activity recognition techniques in ideal conditions.

SENSOR SETUP:
Each sensor provides 3D acceleration (accX,accY,accZ), 3D gyro (gyrX,gyrY,gyrZ), 3D magnetic field orientation (magX,magY,magZ) and 4D quaternions (Q1,Q2,Q3,Q4). The sensors are identified according to the body part on which is placed respectively:

SCENARIOS:
The dataset contains information for three different scenarios depending on whether the sensors are positioned on predefined positions or placed by the users themselves.
- Ideal-placement or the default scenario. The sensors are positioned by the instructor on predefined locations within each body part. The data stemming from this scenario could be considered as the â€œtraining setâ€ for supervised activity recognition systems.
- Self-placement. The user is asked to position a subset of the sensors themselves on the body parts specified by the instructor, but without providing any hint on how the sensors must be exactly placed. This scenario is devised to investigate some of the variability that may occur in the day-to-day usage of an activity recognition system, involving wearable or self-attached sensors. Normally, the self-placement will lead to on-body sensor setups that differ from the ideal-placement. Nevertheless, this difference may be minimal if the subject places the sensor close to the ideal position.
- Induced-displacement. An intentional mispositioning of sensors using rotations and translations with respect to the ideal placement is introduced by the instructor. One of the key interests of including this last scenario is to investigate how the performance of a certain method degrades as the system drifts far from the ideal setup.

A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the documentation facilitated along with the dataset. Also, the papers presented in the section â€œCitation Requestsâ€ provide an insightful description of the dataset and the underlying theory.

Attribute Information:

The dataset comprises the readings of motion sensors recorded while users executed typical daily activities. The detailed format is described in the package. The attributes correspond to raw sensor readings. There is a total of 120 attributes:

We recommend to refer to this dataset as the 'REALDISP dataset' in publications.
We would appreciate if you send us an email (oresti.bl '@' gmail.com) to inform us of any publication using this dataset.